This section is intended to introduce various aspects of the art, which may be associated with exemplary embodiments of the present invention. This discussion is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the present invention. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art.
Seismic inversion is a process of extracting information about the subsurface from data measured at the surface of the Earth during a seismic acquisition survey. In a typical seismic survey, seismic waves are generated by a source 101 positioned at a desired location. As the source generated waves propagate through the subsurface, some of the energy reflects from subsurface interfaces 105, 107, and 109 and travels back to the surface 111, where it is recorded by the receivers 103. The seismic waves 113 and 115 that have been reflected in the subsurface only once before reaching the recording devices are called primary reflections. In contrast, multiple reflections 117 and 119 are the seismic waves that have reflected multiple times along their travel path back to the surface. Surface-related multiple reflections are the waves that have reflected multiple times and incorporate the surface of the Earth or the water surface in their travel path before being recorded.
Full Wavefield Inversion (FWI) is a seismic method capable of utilizing the full seismic record, including the seismic events that are treated as “noise” by standard inversion algorithms. The goal of FWI is to build a realistic subsurface model by minimizing the misfit between the recorded seismic data and synthetic (or modeled) data obtained via numerical simulation.
FWI is a computer-implemented geophysical method that is used to invert for subsurface properties such as velocity or acoustic impedance. The crux of any FWI algorithm can be described as follows: using a starting subsurface physical property model, synthetic seismic data are generated, i.e. modeled or simulated, by solving the wave equation using a numerical scheme (e.g., finite-difference, finite-element etc.). The term velocity model or physical property model as used herein refers to an array of numbers, typically a 3-D array, where each number, which may be called a model parameter, is a value of velocity or another physical property in a cell, where a subsurface region has been conceptually divided into discrete cells for computational purposes. The synthetic seismic data are compared with the field seismic data and using the difference between the two, an error or objective function is calculated. Using the objective function, a modified subsurface model is generated which is used to simulate a new set of synthetic seismic data. This new set of synthetic seismic data is compared with the field data to generate a new objective function. This process is repeated until the objective function is satisfactorily minimized and the final subsurface model is generated. A global or local optimization method is used to minimize the objective function and to update the subsurface model.
The exploration and production industry is moving into frontier oil and gas provinces with higher well costs and drilling risks. In order to execute a drilling program in a safe, efficient and cost effective manner, accurate pre-drill prediction of pore pressure and fracture pressure is useful. In addition, seal integrity analysis is useful in understanding how much hydrocarbons can be trapped by prospect seals before mechanical leak. Conventionally, these analyses have been done using seismic velocities obtained from tomography. Tomographic velocities are used to predict pore pressure using Eaton's normal compaction trend and Bower's effective stress methods. In general, such velocity profiles are suitable for pore pressure prediction in background shales, but are too smooth to detect embedded sand reservoirs. In comparison, FWI provides higher resolution and more accurate velocity models, which provides an opportunity to do pressure prediction for the sands as well. Pressure prediction for sands allows for the appraisal of mechanical seal failure risks of overlying shales.